Catchment‐Scale Shifts in the Magnitude and Partitioning of Carbon Export in Response to Changing Hydrologic Connectivity in a Northern Hardwood Forest
Bibliographic record
Abstract
Abstract The capacity of forest soils to store organic carbon is influenced by changing hydrologic connectivity. We hypothesized that hydrologic connectivity, the water‐mediated transfer of matter and energy between different landscape positions, controls the partitioning between aquatic and atmospheric soil carbon fates. Results from a 5‐year study of a northern hardwood forested catchment indicated that hydrologic connectivity affected both the magnitude and fate of carbon export. Atmospheric carbon export was the major export pathway from the catchment; its rate was regulated by topographic position (i.e., uplands, ecotones, and wetlands) but enhanced or supressed through changes in soil moisture and hydrologic connectivity. Wetter soil conditions reduced CO 2 flux from the ecotones and wetlands where microbial respiration was oxygen‐limited, whereas drier soil conditions that decreased hydrologic connectivity increased CO 2 flux by relieving the oxygen limitation. In contrast, aquatic carbon export was a minor export pathway from the catchment and was driven by hydrologic connectivity, with less carbon export during relatively low discharge years. Past trends suggest a shift to a warmer climate and changes in the timing, duration, and intensity of hydrologic connectivity that are leading to an increase in annual atmospheric carbon export but a decrease in annual aquatic carbon export, despite the intensification of autumn storms. The increase in atmospheric carbon export creates a positive feedback for climate warming that will further disrupt hydrologic connectivity and aquatic carbon export, with consequences for downstream streams and lakes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".